| .. _labeled_faces_in_the_wild_dataset: | |
| The Labeled Faces in the Wild face recognition dataset | |
| ------------------------------------------------------ | |
| This dataset is a collection of JPEG pictures of famous people collected | |
| over the internet, all details are available on the official website: | |
| http://vis-www.cs.umass.edu/lfw/ | |
| Each picture is centered on a single face. The typical task is called | |
| Face Verification: given a pair of two pictures, a binary classifier | |
| must predict whether the two images are from the same person. | |
| An alternative task, Face Recognition or Face Identification is: | |
| given the picture of the face of an unknown person, identify the name | |
| of the person by referring to a gallery of previously seen pictures of | |
| identified persons. | |
| Both Face Verification and Face Recognition are tasks that are typically | |
| performed on the output of a model trained to perform Face Detection. The | |
| most popular model for Face Detection is called Viola-Jones and is | |
| implemented in the OpenCV library. The LFW faces were extracted by this | |
| face detector from various online websites. | |
| **Data Set Characteristics:** | |
| ================= ======================= | |
| Classes 5749 | |
| Samples total 13233 | |
| Dimensionality 5828 | |
| Features real, between 0 and 255 | |
| ================= ======================= | |
| .. dropdown:: Usage | |
| ``scikit-learn`` provides two loaders that will automatically download, | |
| cache, parse the metadata files, decode the jpeg and convert the | |
| interesting slices into memmapped numpy arrays. This dataset size is more | |
| than 200 MB. The first load typically takes more than a couple of minutes | |
| to fully decode the relevant part of the JPEG files into numpy arrays. If | |
| the dataset has been loaded once, the following times the loading times | |
| less than 200ms by using a memmapped version memoized on the disk in the | |
| ``~/scikit_learn_data/lfw_home/`` folder using ``joblib``. | |
| The first loader is used for the Face Identification task: a multi-class | |
| classification task (hence supervised learning):: | |
| >>> from sklearn.datasets import fetch_lfw_people | |
| >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) | |
| >>> for name in lfw_people.target_names: | |
| ... print(name) | |
| ... | |
| Ariel Sharon | |
| Colin Powell | |
| Donald Rumsfeld | |
| George W Bush | |
| Gerhard Schroeder | |
| Hugo Chavez | |
| Tony Blair | |
| The default slice is a rectangular shape around the face, removing | |
| most of the background:: | |
| >>> lfw_people.data.dtype | |
| dtype('float32') | |
| >>> lfw_people.data.shape | |
| (1288, 1850) | |
| >>> lfw_people.images.shape | |
| (1288, 50, 37) | |
| Each of the ``1140`` faces is assigned to a single person id in the ``target`` | |
| array:: | |
| >>> lfw_people.target.shape | |
| (1288,) | |
| >>> list(lfw_people.target[:10]) | |
| [5, 6, 3, 1, 0, 1, 3, 4, 3, 0] | |
| The second loader is typically used for the face verification task: each sample | |
| is a pair of two picture belonging or not to the same person:: | |
| >>> from sklearn.datasets import fetch_lfw_pairs | |
| >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') | |
| >>> list(lfw_pairs_train.target_names) | |
| ['Different persons', 'Same person'] | |
| >>> lfw_pairs_train.pairs.shape | |
| (2200, 2, 62, 47) | |
| >>> lfw_pairs_train.data.shape | |
| (2200, 5828) | |
| >>> lfw_pairs_train.target.shape | |
| (2200,) | |
| Both for the :func:`sklearn.datasets.fetch_lfw_people` and | |
| :func:`sklearn.datasets.fetch_lfw_pairs` function it is | |
| possible to get an additional dimension with the RGB color channels by | |
| passing ``color=True``, in that case the shape will be | |
| ``(2200, 2, 62, 47, 3)``. | |
| The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into | |
| 3 subsets: the development ``train`` set, the development ``test`` set and | |
| an evaluation ``10_folds`` set meant to compute performance metrics using a | |
| 10-folds cross validation scheme. | |
| .. rubric:: References | |
| * `Labeled Faces in the Wild: A Database for Studying Face Recognition | |
| in Unconstrained Environments. | |
| <http://vis-www.cs.umass.edu/lfw/lfw.pdf>`_ | |
| Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. | |
| University of Massachusetts, Amherst, Technical Report 07-49, October, 2007. | |
| .. rubric:: Examples | |
| * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` | |